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Thunderstorm Forecasting with MLFlow Tracking

Develop a robust thunderstorm forecasting system leveraging machine learning models and MLflow for tracking experiments. This project integrates data preparation, model training, hyperparameter tuning, and deployment to predict thunderstorm occurrences, enhancing weather prediction accuracy and enabling proactive safety measures.

16 lectures

What You Will Learn

Mastering data merging and combining techniques using Pandas for weather data.
Implementing machine learning models for thunderstorm prediction.
Applying hyperparameter tuning techniques to optimize model performance.
Utilizing MLflow for tracking and managing machine learning experiments.
Building a modular project structure for maintainability and scalability.
Deploying machine learning models using open source Streamlit Cloud.
Evaluating machine learning model performance using relevant metrics.

System Architecture

Thunderstorm Forecasting with MLFlow Tracking Architecture Diagram

High-level architecture overview of the Thunderstorm Forecasting with MLFlow Tracking .

What You'll Build

  • A data preparation pipeline for cleaning and merging weather data.
  • Machine learning models for predicting thunderstorm occurrences.
  • An MLflow tracking system for managing experiments and model versions.
  • A Streamlit application for visualizing and deploying the thunderstorm forecasting model.
Thunderstorm Forecasting with MLFlow Tracking
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